Aircraft Wildlife Strikes 1990-2023
In [3]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
In [4]:
df = pd.read_csv('STRIKE_REPORTS.csv')

C:\Users\KPMal\AppData\Local\Temp\ipykernel_8900\1840193475.py:1: DtypeWarning: Columns (18,21) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv('STRIKE_REPORTS.csv')
In [5]:
df
Out[5]:
INDEX_NR INCIDENT_DATE INCIDENT_MONTH INCIDENT_YEAR TIME TIME_OF_DAY AIRPORT_ID AIRPORT LATITUDE LONGITUDE ... SIZE NR_INJURIES NR_FATALITIES COMMENTS REPORTED_NAME REPORTED_TITLE SOURCE PERSON LUPDATE TRANSFER
0 608242 6/22/1996 6 1996 NaN NaN KSMF SACRAMENTO INTL 38.69542 -121.59077 ... Medium NaN NaN /Legacy Record=100001/ REDACTED REDACTED Air Transport Report Air Transport Operations 12/20/2007 False
1 608243 6/26/1996 6 1996 NaN NaN KDEN DENVER INTL AIRPORT 39.85841 -104.66700 ... Medium NaN NaN /Legacy Record=100002/ REDACTED REDACTED Air Transport Report Air Transport Operations 12/20/2007 False
2 608244 7/1/1996 7 1996 NaN NaN KOMA EPPLEY AIRFIELD 41.30252 -95.89417 ... Medium NaN NaN /Legacy Record=100003/ REDACTED REDACTED Air Transport Report Air Transport Operations 12/20/2007 False
3 608245 7/1/1996 7 1996 NaN NaN KIAD WASHINGTON DULLES INTL ARPT 38.94453 -77.45581 ... Medium NaN NaN /Legacy Record=100004/ REDACTED REDACTED Air Transport Report Air Transport Operations 12/20/2007 False
4 608246 7/1/1996 7 1996 NaN NaN KLGA LA GUARDIA ARPT 40.77724 -73.87261 ... Medium NaN NaN /Legacy Record=100005/ REDACTED REDACTED Air Transport Report Air Transport Operations 12/20/2007 False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
288805 1472253 8/28/2023 8 2023 21:50 NaN ZZZZ UNKNOWN NaN NaN ... Small NaN NaN NaN REDACTED REDACTED FAA Form 5200-7-E Airport Operations 11/17/2023 False
288806 1472256 8/28/2023 8 2023 20:40 NaN ZZZZ UNKNOWN NaN NaN ... Small NaN NaN NaN REDACTED REDACTED FAA Form 5200-7-E Airport Operations 11/17/2023 False
288807 1472257 8/28/2023 8 2023 07:14 Dusk KDTW DETROIT METRO WAYNE COUNTY ARPT 42.21206 -83.34884 ... Small NaN NaN NaN REDACTED REDACTED FAA Form 5200-7-E Airport Operations 11/17/2023 False
288808 1472258 8/28/2023 8 2023 09:00 Day KPIH POCATELLO REGIONAL ARPT 42.91131 -112.59586 ... Medium NaN NaN NaN REDACTED REDACTED FAA Form 5200-7-E Airport Operations 11/17/2023 False
288809 1472259 8/28/2023 8 2023 10:15 NaN KAMA RICK HUSBAND AMARILLO INTL 35.21937 -101.70593 ... Small NaN NaN NaN REDACTED REDACTED FAA Form 5200-7-E Carcass Found 11/17/2023 False

288810 rows × 100 columns

In [6]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 288810 entries, 0 to 288809
Data columns (total 100 columns):
 #   Column                 Non-Null Count   Dtype  
---  ------                 --------------   -----  
 0   INDEX_NR               288810 non-null  int64  
 1   INCIDENT_DATE          288810 non-null  object 
 2   INCIDENT_MONTH         288810 non-null  int64  
 3   INCIDENT_YEAR          288810 non-null  int64  
 4   TIME                   163633 non-null  object 
 5   TIME_OF_DAY            166394 non-null  object 
 6   AIRPORT_ID             288804 non-null  object 
 7   AIRPORT                288810 non-null  object 
 8   LATITUDE               253309 non-null  float64
 9   LONGITUDE              253308 non-null  float64
 10  RUNWAY                 218852 non-null  object 
 11  STATE                  253309 non-null  object 
 12  FAAREGION              253309 non-null  object 
 13  LOCATION               33939 non-null   object 
 14  ENROUTE_STATE          5037 non-null    object 
 15  OPID                   288784 non-null  object 
 16  OPERATOR               288810 non-null  object 
 17  REG                    172543 non-null  object 
 18  FLT                    129249 non-null  object 
 19  AIRCRAFT               288810 non-null  object 
 20  AMA                    205475 non-null  object 
 21  AMO                    190159 non-null  object 
 22  EMA                    192151 non-null  float64
 23  EMO                    186756 non-null  float64
 24  AC_CLASS               206893 non-null  object 
 25  AC_MASS                206490 non-null  float64
 26  TYPE_ENG               206044 non-null  object 
 27  NUM_ENGS               206259 non-null  float64
 28  ENG_1_POS              205481 non-null  float64
 29  ENG_2_POS              191612 non-null  float64
 30  ENG_3_POS              13080 non-null   float64
 31  ENG_4_POS              3290 non-null    float64
 32  PHASE_OF_FLIGHT        177843 non-null  object 
 33  HEIGHT                 148807 non-null  float64
 34  SPEED                  94237 non-null   float64
 35  DISTANCE               188234 non-null  float64
 36  SKY                    139382 non-null  object 
 37  PRECIPITATION          11175 non-null   object 
 38  AOS                    14196 non-null   float64
 39  COST_REPAIRS           5040 non-null    object 
 40  COST_OTHER             4760 non-null    object 
 41  COST_REPAIRS_INFL_ADJ  5040 non-null    object 
 42  COST_OTHER_INFL_ADJ    4760 non-null    object 
 43  INGESTED_OTHER         288810 non-null  bool   
 44  INDICATED_DAMAGE       288810 non-null  bool   
 45  DAMAGE_LEVEL           187315 non-null  object 
 46  STR_RAD                288810 non-null  bool   
 47  DAM_RAD                288810 non-null  bool   
 48  STR_WINDSHLD           288810 non-null  bool   
 49  DAM_WINDSHLD           288810 non-null  bool   
 50  STR_NOSE               288810 non-null  bool   
 51  DAM_NOSE               288810 non-null  bool   
 52  STR_ENG1               288810 non-null  bool   
 53  DAM_ENG1               288810 non-null  bool   
 54  ING_ENG1               288810 non-null  bool   
 55  STR_ENG2               288810 non-null  bool   
 56  DAM_ENG2               288810 non-null  bool   
 57  ING_ENG2               288810 non-null  bool   
 58  STR_ENG3               288810 non-null  bool   
 59  DAM_ENG3               288810 non-null  bool   
 60  ING_ENG3               288810 non-null  bool   
 61  STR_ENG4               288810 non-null  bool   
 62  DAM_ENG4               288810 non-null  bool   
 63  ING_ENG4               288810 non-null  bool   
 64  STR_PROP               288810 non-null  bool   
 65  DAM_PROP               288810 non-null  bool   
 66  STR_WING_ROT           288810 non-null  bool   
 67  DAM_WING_ROT           288810 non-null  bool   
 68  STR_FUSE               288810 non-null  bool   
 69  DAM_FUSE               288810 non-null  bool   
 70  STR_LG                 288810 non-null  bool   
 71  DAM_LG                 288810 non-null  bool   
 72  STR_TAIL               288810 non-null  bool   
 73  DAM_TAIL               288810 non-null  bool   
 74  STR_LGHTS              288810 non-null  bool   
 75  DAM_LGHTS              288810 non-null  bool   
 76  STR_OTHER              288810 non-null  bool   
 77  DAM_OTHER              288810 non-null  bool   
 78  OTHER_SPECIFY          38462 non-null   object 
 79  EFFECT                 13718 non-null   object 
 80  EFFECT_OTHER           2221 non-null    object 
 81  BIRD_BAND_NUMBER       390 non-null     float64
 82  SPECIES_ID             288806 non-null  object 
 83  SPECIES                288809 non-null  object 
 84  REMARKS                264305 non-null  object 
 85  REMAINS_COLLECTED      288810 non-null  bool   
 86  REMAINS_SENT           288810 non-null  bool   
 87  WARNED                 288810 non-null  object 
 88  NUM_SEEN               94773 non-null   object 
 89  NUM_STRUCK             288174 non-null  object 
 90  SIZE                   263538 non-null  object 
 91  NR_INJURIES            276 non-null     float64
 92  NR_FATALITIES          24 non-null      float64
 93  COMMENTS               218834 non-null  object 
 94  REPORTED_NAME          288810 non-null  object 
 95  REPORTED_TITLE         288810 non-null  object 
 96  SOURCE                 288810 non-null  object 
 97  PERSON                 267676 non-null  object 
 98  LUPDATE                288810 non-null  object 
 99  TRANSFER               288810 non-null  bool   
dtypes: bool(37), float64(17), int64(3), object(43)
memory usage: 149.0+ MB
In [7]:
df.describe()
Out[7]:
INDEX_NR INCIDENT_MONTH INCIDENT_YEAR LATITUDE LONGITUDE EMA EMO AC_MASS NUM_ENGS ENG_1_POS ENG_2_POS ENG_3_POS ENG_4_POS HEIGHT SPEED DISTANCE AOS BIRD_BAND_NUMBER NR_INJURIES NR_FATALITIES
count 2.888100e+05 288810.000000 288810.000000 2.533090e+05 253308.000000 192151.000000 186756.000000 206490.000000 206259.000000 205481.000000 191612.000000 13080.000000 3290.000000 148807.000000 94237.000000 188234.000000 14196.000000 3.900000e+02 276.000000 24.000000
mean 8.736693e+05 7.175974 2011.697798 2.012754e+02 -90.880247 19.755812 8.522377 3.538312 2.013929 2.814956 2.667536 2.900306 2.043161 865.960358 142.585057 0.814466 91.454416 8.811022e+07 1.293478 2.041667
std 2.526448e+05 2.765082 8.432311 8.177522e+04 327.243918 10.724278 12.866405 0.867312 0.420133 2.113806 1.981032 1.950649 1.435114 1843.098801 46.650050 3.547950 661.074472 9.110186e+07 0.756134 1.654484
min 6.082420e+05 1.000000 1990.000000 -3.767333e+01 -177.381000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000e+00 1.000000 1.000000
25% 6.893202e+05 5.000000 2006.000000 3.298764e+01 -98.469780 10.000000 1.000000 3.000000 2.000000 1.000000 1.000000 1.000000 1.000000 0.000000 120.000000 0.000000 1.000000 0.000000e+00 1.000000 1.000000
50% 7.646935e+05 7.000000 2014.000000 3.880581e+01 -87.904460 22.000000 4.000000 4.000000 2.000000 1.000000 1.000000 1.000000 1.000000 50.000000 140.000000 0.000000 3.000000 7.897730e+07 1.000000 1.500000
75% 1.064714e+06 9.000000 2019.000000 4.085010e+01 -80.417940 31.000000 10.000000 4.000000 2.000000 5.000000 5.000000 5.000000 4.000000 900.000000 160.000000 0.000000 24.000000 1.893343e+08 1.000000 2.000000
max 1.472259e+06 12.000000 2023.000000 4.115443e+07 164140.000000 92.000000 2301.000000 5.000000 4.000000 7.000000 7.000000 6.000000 5.000000 31300.000000 1250.000000 99.000000 62848.000000 2.821218e+08 7.000000 8.000000
In [8]:
# Dropping duplicate data
df.drop_duplicates(inplace=True)
In [9]:
df.shape
Out[9]:
(288810, 100)
In [10]:
# Incidents by year
groupedYr_df = df.groupby('INCIDENT_YEAR').size().reset_index(name='Count')
In [11]:
groupedYr_df
Out[11]:
INCIDENT_YEAR Count
0 1990 2120
1 1991 2516
2 1992 2651
3 1993 2624
4 1994 2708
5 1995 2825
6 1996 3031
7 1997 3559
8 1998 3809
9 1999 5120
10 2000 6024
11 2001 5825
12 2002 6222
13 2003 5992
14 2004 6606
15 2005 7270
16 2006 7295
17 2007 7751
18 2008 7638
19 2009 9509
20 2010 9898
21 2011 10110
22 2012 10932
23 2013 11410
24 2014 13692
25 2015 13772
26 2016 13323
27 2017 14776
28 2018 16202
29 2019 17341
30 2020 11620
31 2021 15638
32 2022 17226
33 2023 11775
In [19]:
sns.set(rc={'figure.figsize':(20,15)})
#sns.set_style("ticks")
sns.set(rc={'axes.facecolor':'#000000', 'figure.facecolor':'#EBEBEB'})
sns.barplot(groupedYr_df, x="INCIDENT_YEAR", y="Count")
#plt.show(bar_plot)

C:\Users\KPMal\anaconda3\envs\Py3-9\lib\site-packages\seaborn\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
C:\Users\KPMal\anaconda3\envs\Py3-9\lib\site-packages\seaborn\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
C:\Users\KPMal\anaconda3\envs\Py3-9\lib\site-packages\seaborn\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
Out[19]:
<Axes: xlabel='INCIDENT_YEAR', ylabel='Count'>
Aircarft Wildlife Strikes in the U.S.
In [24]:
fig = go.Figure(data=go.Scattergeo(
        lon = df['LONGITUDE'],
        lat = df['LATITUDE'],
        text = df['INCIDENT_YEAR'],
        mode = 'markers',
        marker_color = df['NR_FATALITIES'],
        ))


fig.update_layout(
        title = 'U.S. Strike Locations',
        geo_scope='usa',
    )

fig.show()
In [ ]: